Microsoft AI-901 - Microsoft Azure AI Fundamentals (Updated Version)

Providing users with clear information about an AI system’s purpose, functionality, and limitations is an example of the Microsoft responsible AI principle of transparency . Transparency means users should be able to understand when and how AI is being used, what the system can do, and what its limitations are.

A schema defines which fields to extract when analyzing content.

In Azure Content Understanding, the schema or fieldSchema defines the structured data that the analyzer extracts from content, including field names, types, and extraction behavior. Microsoft documentation states that Content Understanding lets you define a schema to extract, classify, or generate field values from unstructured content.

The other options are incorrect:

A keyword list does not define the complete structured output fields.

OCR-only processing extracts text, but it does not define structured fields.

A synchronous API call describes a request pattern, not the extraction schema.

To identify the main talking points in customer feedback surveys, use key phrase extraction . This feature identifies important words and phrases in text, which is useful for summarizing themes in feedback.

The described solution coordinates a multi-step workflow : gathering user preferences, searching for options, requesting approval, booking services, and maintaining context across steps. This is an example of Agentic AI .

Agentic AI systems use AI agents to reason over user goals, maintain context, decide which actions to take, and use tools or services to complete tasks across multiple steps.

Why the other options are incorrect:

A. information extraction extracts structured data from content.

C. computer vision analyzes images or video.

D. generative AI creates new content, but the scenario specifically describes an autonomous, context-aware workflow with actions, which is Agentic AI .

The correct answer is API endpoint . After a generative AI model is deployed in Microsoft Foundry, the deployment exposes an inference endpoint through which external applications can submit requests and receive generated responses. Microsoft Foundry documentation describes deployed models as being accessible programmatically through an endpoint and associated credentials. Applications invoke the relevant REST API or SDK against this endpoint and specify the deployed model as required by the API.

An embedding vector is a numerical representation of semantic information used for similarity search, retrieval, and grounding; it is not the network interface through which a deployed model is invoked. A training dataset contains data used to train or fine-tune a model and is not called when performing inference. A URL parameter can be part of an API request, but it does not represent the deployed model ' s callable interface.

This directly aligns with the AI-901 objective to deploy a model and interact with it in the Foundry portal and to create lightweight applications that invoke deployed generative AI models through supported APIs and SDKs.

To minimize unintended bias, the training data should be representative of the population for which the model will be used. Microsoft responsible AI guidance identifies fairness as a key principle and notes that discrepancies can occur when systems underperform for specific demographic groups or rarely observed input conditions in training data.

A global insurance dataset may not represent UK drivers. A completely random sample may still underrepresent important groups. Removing protected characteristics alone does not guarantee fairness, because other fields can act as proxies. The best answer is to use a training sample representative of the target UK population.

The system must not discriminate based on gender, race, or age. = Fairness

This requirement maps to fairness because fairness means AI systems should avoid unfair bias and treat people equitably across demographic groups.

Personal data must be visible only to approved users. = Privacy and security

This requirement maps to privacy and security because it focuses on protecting personal data and restricting access to authorized users only.

Automated decision-making processes must be recorded so that approved users can identify why a decision was made. = Transparency

This requirement maps to transparency because recording decision-making processes helps users understand and explain how or why an AI-supported decision was made.

Ongoing monitoring, feedback, and evaluation are goals of the Reliability & Safety principle.

The Microsoft responsible AI principle of Reliability and safety focuses on ensuring that AI systems operate consistently, safely, and as intended across different conditions. This includes testing, monitoring, collecting feedback, and evaluating system performance over time.

Why the other options are incorrect:

Human oversight and control aligns more closely with accountability.

Data governance and management aligns more closely with privacy and security.

The minimization of stereotyping, demeaning, and erasing outputs aligns more closely with fairness and inclusiveness.

The requirement is to convert written customer notifications into natural-sounding spoken audio . This is speech synthesis , also known as text to speech .

Microsoft’s Azure Speech documentation describes text to speech as a capability that converts text into natural-sounding synthesized speech. Therefore, for playing written notifications over a phone system, the correct Azure Speech capability is speech synthesis .

Why the other options are incorrect:

A. speaker recognition identifies or verifies speakers by voice.

C. speech recognition converts spoken audio into text.

D. speech translation translates spoken audio between languages.